Cancer Treatment Decisions Research Articles

A preoperative predictive model based on multi-modal features to predict pathological complete response after neoadjuvant chemoimmunotherapy in esophageal cancer patients

BackgroundThis study aimed to develop a multi-modality model by incorporating pretreatment computed tomography (CT) radiomics and pathomics features along with clinical variables to predict pathologic complete response (pCR) to neoadjuvant chemoimmunotherapy in patients with locally advanced esophageal cancer (EC).MethodA total of 223 EC patients who underwent neoadjuvant chemoimmunotherapy followed by surgical intervention between August 2021 and December 2023 were included in this study. Radiomics features were extracted from contrast-enhanced CT images using PyrRadiomics, while pathomics features were derived from whole-slide images (WSIs) of pathological specimens using a fine-tuned deep learning model (ResNet-50). After feature selection, three single-modality prediction models and a combined multi-modality model integrating two radiomics features, 11 pathomics features, and two clinicopathological features were constructed using the support vector machine (SVM) algorithm. The performance of the models were evaluated using receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analysis (DCA). Shapley values were also utilized to explain the prediction model.ResultsThe predictive capability of the multi-modality model in predicting pCR yielded an area under the curve (AUC) of 0.89 (95% confidence interval [CI], 0.75-1.00), outperforming the radiomics model (AUC 0.70 [95% CI 0.54-0.85]), pathomics model (AUC 0.77 [95% CI 0.53-1.00]), and clinical model (AUC 0.63 [95% CI 0.46-0.80]). Additionally, both the calibration plot and DCA curves support the clinical utility of the integrated multi-modality model.ConclusionsThe combined multi-modality model we propose can better predict the pCR status of esophageal cancer and help inform clinical treatment decisions.

Harnessing Artificial Intelligence to Enhance Global Breast Cancer Care: A Scoping Review of Applications, Outcomes, and Challenges.

In recent years, Artificial Intelligence (AI) has shown transformative potential in advancing breast cancer care globally. This scoping review seeks to provide a comprehensive overview of AI applications in breast cancer care, examining how they could reshape diagnosis, treatment, and management on a worldwide scale and discussing both the benefits and challenges associated with their adoption. In accordance with PRISMA-ScR and ensuing guidelines on scoping reviews, PubMed, Web of Science, Cochrane Library, and Embase were systematically searched from inception to end of May 2024. Keywords included "Artificial Intelligence" and "Breast Cancer". Original studies were included based on their focus on AI applications in breast cancer care and narrative synthesis was employed for data extraction and interpretation, with the findings organized into coherent themes. Finally, 84 articles were included. The majority were conducted in developed countries (n = 54). The majority of publications were in the last 10 years (n = 83). The six main themes for AI applications were AI for breast cancer screening (n = 32), AI for image detection of nodal status (n = 7), AI-assisted histopathology (n = 8), AI in assessing post-neoadjuvant chemotherapy (NACT) response (n = 23), AI in breast cancer margin assessment (n = 5), and AI as a clinical decision support tool (n = 9). AI has been used as clinical decision support tools to augment treatment decisions for breast cancer and in multidisciplinary tumor board settings. Overall, AI applications demonstrated improved accuracy and efficiency; however, most articles did not report patient-centric clinical outcomes. AI applications in breast cancer care show promise in enhancing diagnostic accuracy and treatment planning. However, persistent challenges in AI adoption, such as data quality, algorithm transparency, and resource disparities, must be addressed to advance the field.

Understanding Cancer Treatment Decision Making Among Cancer Survivors: Weighing Cancer Recurrence Versus Cardiotoxicity.

Nearly 20% of US cancer survivors develop cardiovascular disease (CVD) from cardiotoxic cancer treatments. Patients and providers may consider alternative treatments to lower cardiotoxicity risk, but these may be less effective at preventing relapse/recurrence, presenting a difficult tradeoff. This study explored survivors' cancer treatment decision-making when weighing this tradeoff. Using adjusted multivariable logistic regression, we examined 443 US survivors' risk perceptions (deliberative, affective, and intuitive) about cancer and CVD and associations of these with their selection between two hypothetical cancer treatments: Treatment A: 5% chance of cancer recurrence and 10% chance of CVD; Treatment B: 10% chance of recurrence and 5% chance of CVD. We explored the effects of delay discounting by randomizing to a condition describing cancer recurrence/CVD as either immediate or delayed. More survivors (Mage=48, range=18-93; M=10.8years post-diagnosis) selected Treatment A than Treatment B (72% v. 28%). Timing of onset was not associated with treatment selection. Greater affective risk perception (worry) about cancer was associated with increased odds of choosing Treatment A, whereas greater CVD worry was associated with decreased odds (OR-cancer=1.33, p=0.006; OR-CVD=0.72, p=0.007). Neither deliberative nor experiential risk perceptions were associated with treatment choice. Survivors were more likely to select the treatment that minimized recurrence rather than CVD-regardless of the timing of onset. Treatment decision was linked to both cancer- and CVD-related worry but not deliberative or experiential risk perceptions. During treatment discussions, clinicians should open conversations about the risks of treatment-associated cardiotoxicity, the probabilities, and patients' relative worries about cancer and cardiotoxicity.

Novel Predictive Strategy Using CA19-9 and Fecal Elastase Levels to Make Treatment Decisions for Resectable Pancreatic Cancer: A Retrospective Study.

Background: Carbohydrate antigen 19-9 (CA19-9) is used as a marker to predict recurrence and survival of patients with pancreatic ductal adenocarcinoma (PDAC). Recently, fecal elastase-1 (FE-1) has been shown to correlate with prognosis in patients with PDAC. Method: A total of 536 patients who underwent curative intent surgery between 2010 and 2019 were included in the study. The cutoff points of preoperative CA19-9 and FE-1 levels were extracted from the Youden index and previous studies. Cox proportional hazard models were used to investigate the association between preoperative tumor marker levels and survival after surgery. Results: Patients with CA19-9 ≥ 385 had more advanced T-/N-stages and lower survival rates compared to those with CA19-9 < 385. Multivariate Cox analyses demonstrated that combining preoperative tumor markers was associated with worse 3-year overall survival (both CA19-9 and FE-1 low, HR = 1.41, p = 0.044; both high, HR = 1.44, p = 0.047; CA19-9 high and FE-1 low, HR = 2.00, p < 0.001; and p for trend < 0.001). The same trend was confirmed in the analysis with recurrence-free survival. Conclusions: This study presents a new predictive strategy using combined CA19-9 and FE-1 levels to determine the treatment for resectable pancreatic cancer.

Combined effect of frailty and sarcopenia on postoperative complications in older adults undergoing curative surgery for hepato‐biliary‐pancreatic cancer

AbstractAimOlder adults with cancer are often at increased risk for postoperative complications following major surgeries. This study aimed to evaluate the combined role of frailty and sarcopenia in predicting postoperative complications in older adults with hepatobiliary and pancreatic cancer undergoing surgery.MethodsThis retrospective study included 107 Japanese patients who underwent comprehensive geriatric assessment (CGA) at the geriatric oncology service before cancer treatment decisions and subsequent curative surgery for hepatobiliary and pancreatic cancer. Frailty status was measured using the validated 10‐item frailty index based on a CGA (FI‐CGA‐10) and categorized as fit, prefrail, or frail. Sarcopenia was assessed using bioelectrical impedance analysis and grip strength. The primary outcome was postoperative complications, defined as Clavien–Dindo grade ≥ III, within 1 month of surgery.ResultsThe median age of the 107 patients was 79 (range, 75–89) years. Patients were categorized as fit (n = 36, 33.7%), prefrail (n = 57, 53.2%), or frail (n = 14, 13.1%). Of these, 21 patients (20%) were diagnosed with sarcopenia; 16 patients (15%) experienced postoperative complications. Patients classified as prefrail or frail had a higher incidence of postoperative complications compared with those classified as fit (19.7% vs. 5.6%, p = 0.08). Patients with both prefrail or frail and sarcopenia had a significantly higher risk of postoperative complications. This association remained significant in the multivariable model (OR 4.74; 95% CI, 1.10–20.29; p = 0.04).ConclusionIn this study, patients classified as prefrail/frail and sarcopenic were at significantly higher risk for postoperative complications.

Limited Efficacy of Anti-EGFR Monoclonal Antibodies in Colorectal Cancer Patients with Rare RAS Variants: Analysis of the C-CAT Database.

Epidermal growth factor receptor (EGFR) inhibition is crucial in treating RAS wild-type metastatic colorectal cancer, yet current testing methods may miss rare RAS variants affecting treatment efficacy. We analyzed 4122 colorectal cancer patients receiving anti-EGFR antibodies from the Center for Cancer Genomics and Advanced Therapeutics database, identifying 54 patients (1.3%) with rare RAS variants undetectable by standard testing. These patients showed significantly lower response rates to anti-EGFR therapy (28.3%) compared to RAS wild-type cases (44.6%, p = 0.003). Disease control rates were also lower in rare variant cases (60.9%) versus wild-type cases (80.0%). Most common rare variants included KRAS Q22K, A59E, and A11_G12insGA. Comprehensive genomic profiling revealed additional alterations in TP53 (90.7%), APC (87.0%), and non-V600E BRAF mutations (25.9%). Our findings suggest that rare RAS variants predict poor anti-EGFR therapy response, highlighting the potential benefit of comprehensive genomic profiling before treatment initiation. This study provides real-world evidence supporting the clinical relevance of rare RAS variants in treatment decision-making for colorectal cancer. Future studies should focus on developing cost-effective comprehensive testing strategies and evaluating alternative treatment approaches for patients with rare RAS variants.

Prognostic Performance of the Next-Generation Sequencing-Based Multigene Assay in Early Breast Cancer Patients Treated According to the 21-Gene Assay Results.

Multigene assays guide treatment decisions in early-stage hormone receptor-positive breast cancer. OncoFREE, a next-generation sequencing assay using 179 genes, was developed for this purpose. This study aimed to evaluate the concordance between the Oncotype DX (ODX) Recurrence Score (RS) and the OncoFREE Decision Index (DI) and to compare their performance. We retrospectively collected tumor blocks from patients who underwent ODX and treatment between 2012 and 2022 at four tertiary hospitals and performed OncoFREE on these samples. Distant metastasis-free survival (DMFS) was compared using RS and DI, with score cut-offs of 25 and 20, respectively. Among 838 patients, a strong correlation was observed between RS and DI (Pearson correlation coefficient 0.83). At a median follow-up of 54 months, patients with high DI had significantly worse DMFS compared to those with low DI (log-rank p < 0.001, hazard ratio [HR] 5.73, 95% confidence interval [CI] 1.87-17.57; multivariable p=0.048, HR 3.45, 95% CI 1.01-11.76). In 513 patients aged ≤50 years, DMFS was significantly different as a function of DI (p=0.035, HR 3.98, 95% CI 1.00-15.89) but not RS (p=0.792). Among 376 patients aged ≤50 years who avoided chemotherapy based on low RS, 64 with high DI had worse DMFS (p=0.015, HR 5.91, 95% CI 1.17-29.78). OncoFREE showed strong concordance with ODX and effectively identified high-risk patients, particularly in younger individuals. It could be an affordable alternative to ODX for guiding treatment in hormone receptor-positive early breast cancer.

Predicting Tumor Progression in Patients with Cervical Cancer Using Computer Tomography Radiomic Features

The objective of this study was to evaluate the effectiveness of utilizing radiomic features from radiation planning computed tomography (CT) scans in predicting tumor progression among patients with cervical cancers. A retrospective analysis was conducted on individuals who underwent radiotherapy for cervical cancer between 2015 and 2020, utilizing an institutional database. Radiomic features, encompassing first-order statistical, morphological, Gray-Level Co-Occurrence Matrix (GLCM), Gray-Level Run Length Matrix (GLRLM), and Gray-Level Dependence Matrix (GLDM) features, were extracted from the primary cervical tumor on the CT scans. The study encompassed 112 CT scans from patients with varying stages of cervical cancer ((FIGO Staging of Cervical Cancer 2018): 24% at stage I, 47% at stage II, 21% at stage III, and 10% at stage IV). Of these, 31% (n = 35/112) exhibited tumor progression. Univariate feature analysis identified three morphological features that displayed statistically significant differences (p &lt; 0.05) between patients with and without progression. Combining these features enabled a classification model to be developed with a mean sensitivity, specificity, accuracy, and AUC of 76.1% (CI 1.5%), 70.4% (CI 4.1%), 73.6% (CI 2.1%), and 0.794 (CI 0.029), respectively, employing nested ten-fold cross-validation. This research highlights the potential of CT radiomic models in predicting post-radiotherapy tumor progression, offering a promising approach for tailoring personalized treatment decisions in cervical cancer.

A cross-sectional analysis to characterise treatment decision making for advanced cancer at a tertiary treatment centre: Where can we improve the process?

In decisions relating to cancer treatment, the risks and benefits of treatment and the patient's preferences must be considered to ensure concordance with goals of care. Shared decision making (SDM) can facilitate these discussions and is associated with reduced decision conflict. This study aimed to characterise decision making for advanced cancer patients at a UK tertiary cancer centre and identify who may be at risk of suboptimal SDM and increased decision conflict. Participants completed the SDM-Q-9, decision conflict and decision self-efficacy scale following a consultation where an advanced cancer treatment decision was made. Pearson's chi-square test identified patient characteristics associated with SDM-Q-9, decision self-efficacy and decision conflict score categories; odds ratios were calculated to determine which patients were at increased probability of experiencing suboptimal SDM, low decision self-efficacy or high decision conflict. Participant's (n=211) scores indicated predominantly high SDM, high decision self-efficacy and low decision conflict. Patient gender and the presence of an informal caregiver in consultation were significantly associated with SDM-Q-9 score category (p>0.05). Female patients (OR=2.466, 95% CI: 1.223-4.974) and those attending consultations alone (OR=0.440, 95% CI: 0.222-0.874) had greater odds of reporting lower SDM scores. High SDM scores indicate either effective SDM behaviours or satisfaction with care biasing responses. Greater support to engage with SDM is required for female patients and those who attend alone in advanced cancer treatment decision consultations.

A next generation probabilistic approach to analyze cancer patients data with inference and applications

This study addresses the critical challenges faced in cancer care, particularly in predicting survival times for patients with lung cancer and acute myeloid leukemia. Despite recent advancements in medical science, existing models often fall short in accurately capturing disease progression, leading to less effective clinical decision-making, and compromised patient outcomes. The need for advanced predictive models is urgent to improve survival time forecasts and enhance treatment strategies. In response to this, we introduce a novel probabilistic approach, the New Weibull (NEWE) model, which is part of a newly generated class designed to model cancer patient data more effectively. Our methodology includes using seven well-known estimation methods, each rigorously evaluated for consistency through Monte Carlo simulation studies focused on key metrics such as absolute bias, mean square error, and mean relative error. The datasets analyzed include survival times for twenty acute myeloid leukemia patients, 121 breast cancer patients from 1929 to 1938, 33 patients with acute myelogenous leukemia, data from eighteen individuals who died from causes unrelated to cancer, and survival times of advanced lung cancer patients undergoing standard chemotherapy. The NEWE model outperformed competing models, particularly in Anderson-Darling, Cramer-von Mises, and Kolmogorov-Smirnov tests, with significantly higher p-values. These findings highlight the NEWE model’s potential to transform predictive oncology by offering more precise survival time predictions, improving the quality of care and decision-making in cancer treatment.

Cost-Utility Analysis of Multigene Assays to Guide Treatment Decisions for Node-Negative Early Breast Cancer.

Clinicopathologic and patient factors, such as tumor grade, size, age, and menopausal status, provide limited prognostic and predictive information in hormone receptor positive (HR +), human epidermal growth receptor 2 negative (HER2-), node-negative early-stage breast cancer, leading to potential over- or under-treatment. Multigene expression profile tests used in clinical practice in the USA, including the 21-gene assay, 70-gene assay, 12-gene assay, and 50-gene assay, offer prognostic information beyond traditional clinicopathologic features to improve treatment decisions. This study aimed to estimate the cost-effectiveness of these four multigene assays compared with clinicopathologic risk assessment alone. A decision tree categorized hypothetical patients with HR + /HER2- early-stage invasive breast cancer according to clinical and genomic risk, and integrated clinical expert insights for chemotherapy allocation with literature inputs. A Markov model simulated lifetime costs and outcomes of chemotherapy decisions over a patient's lifetime. The probability of distant breast cancer recurrence was derived from TAILORx (21-gene assay), MINDACT (70-gene assay), and TransATAC (12-gene assay, 50-gene assay) studies. Costs were calculated from a US societal perspective in 2021 US dollars, considering healthcare costs, lost productivity, and patient out-of-pocket expenses. The 21-gene assay and 50-gene assay were less costly ( -$12,189 and -$2410, respectively) and more effective [0.23 and 0.07 quality-adjusted life years (QALYs), respectively] compared with clinicopathologic risk alone. Similarly, the 70-gene assay and 12-gene assay are also cost-effective alternatives [incremental cost-effectiveness ratio (ICER): 27,760 and 7942, respectively]. All four multigene assays were cost-effective from a societal perspective, offering low net lifetime costs or savings with improved outcomes compared with clinicopathologic risk assessment alone. These assays can help refine treatment decisions by providing prognostic risk estimates. In the case of the 21-gene assay, it can also predict chemotherapy benefit leading to the highest lifetime cost savings and greatest QALY gain.

Use of Adaptive Conjoint Analysis-Based Values Clarification in a Patient Decision Aid Is Not Associated with Better Perceived Values Clarity or Reduced Decisional Conflict but Enhances Values Congruence.

Evidence is lacking on the most effective values clarification methods (VCMs) in patient decision aids (PtDAs). We tested the effects of an adaptive conjoint analysis (ACA)-based VCM compared with a ranking-based VCM and no VCM on several decision-related outcomes, with the decisional conflict and its subscale "perceived values clarity" as primary outcomes. Online experimental study with 3 conditions: no VCM versus ranking-based VCM versus ACA-based VCM (N = 282; Mage = 63.11 y, s = 12.12), with the latter 2 conditions including attributes important for a lung cancer treatment decision. We assessed 1) decisional conflict, 2) perceived values clarity (decisional conflict subscale), 3) perceived cognitive load, 4) anticipated regret, 5) ambivalence, 6) preparedness for decision making, 7) hypothetical treatment preference, and 8) values congruence (proxy). We performed analysis of covariance and linear regression. Age and level of deliberation were included as potential moderators, and we controlled for subjective numeracy (covariate). We exploratively tested the moderating effects of subjective numeracy and health literacy (without covariates). We found no significant effect of type of VCM on overall decisional conflict or perceived values clarity. Age had a moderating effect: in younger participants, no VCM (v. ranking-based VCM) led to more values clarity, while in older participants, a ranking-based VCM (v. no VCM) led to more values clarity. Completing the ACA-based VCM, compared with no VCM, resulted in more values congruence. The hypothetical choice situation might have induced lower levels of cognitive/affective involvement in the decision. This study found mixed effects of an ACA-based VCM. It did not decrease decisional conflict or increase perceived values clarity, yet it did improve values congruence. Completion of an ACA-based VCM in a PtDA may increase values congruence. An adaptive conjoint analysis or a ranking-based values clarification method did not decrease analog patients' decisional conflict nor did it increase their perceived values clarity.In younger participants, no VCM (v. ranking-based VCM) led to more values clarity, while in older participants, a ranking-based VCM (v. no VCM) led to more values clarity.An adaptive conjoint analysis task for values clarification resulted in more values congruence.

Deep weighted survival neural networks to survival risk prediction

Survival risk prediction models have become important tools for clinicians to improve cancer treatment decisions. In the medical field, using gene expression data to build deep survival neural network models significantly improves accurate survival prognosis. However, it still poses a challenge in building an efficient method to improve the accuracy of cancer-specific survival risk prediction, such as data noise problem. In order to solve the above problem, we propose a diversity reweighted deep survival neural network method with grid optimization (DRGONet) to improve the accuracy of cancer-specific survival risk prediction. Specifically, reweighting can be employed to adjust the weights assigned to each data point in the dataset based on their importance or relevance, thereby mitigating the impact of noisy or irrelevant data and improving model performance. Incorporating diversity into the goal of multiple learning models can help minimize bias and improve learning outcomes. Furthermore, hyperparameters can be optimized with grid optimization. Experimental results have demonstrated that our proposed approach has significant advantages (improved about 5%) in real-world medical scenarios, outperforming state-of-the-art comparison methods by a large margin. Our study highlights the significance of using DRGONet to overcome the limitations of building accurate survival prediction models. By implementing our technique in cancer research, we hope to reduce the suffering experienced by cancer patients and improve the effectiveness of treatment.

OGC SO04 - Impact of COVID-19 on Decision-making and Survival in Esophago-Gastric Cancer Treatment: Insights from a Multidisciplinary Approach

Abstract Background The COVID-19 pandemic necessitated rapid adaptation in healthcare delivery, affecting cancer care pathways. This study assesses the impact of these changes on multidisciplinary team (MDT) decision-making and survival outcomes in esophago-gastric cancer patients across pre-pandemic, peak pandemic, and post-pandemic periods. Method A retrospective cohort study involving patients referred to a UK tertiary EG cancer center during distinct six-month intervals mirroring the pandemic's evolution. Analysis focused on MDT recommendations, treatment pathways, and two-year survival rates, comparing deviations from standard care across periods. Results The study revealed a significant reduction in patient referrals and expedited treatment decisions during the pandemic peak, with a shift towards palliative care in post-pandemic cases. Although no statistically significant difference in two-year survival rates was observed, the post-pandemic period showed an increase in tumor aggressiveness and positive resection margins, suggesting potential long-term impacts of delayed diagnosis and treatment. Conclusion COVID-19 has significantly influenced MDT decision-making and patient pathways in esophago-gastric cancer care, underlining the necessity of resilient healthcare systems. Despite maintaining survival rates, the observed increase in tumor aggressiveness post-pandemic highlights the critical need for uninterrupted cancer diagnosis and treatment services during global health emergencies.

Comparative prognostic value of tumor volume in IOIO and IOTKI treatment for metastatic renal cancer

ObjectivesWe aimed to investigate the prognostic significance of tumor size in metastatic renal cell carcinoma (mRCC) by comparing the effectiveness of dual immune checkpoint inhibitor (IOIO) and immune checkpoint inhibitor combined with tyrosine kinase inhibitor (IOTKI) therapies. MethodsThis retrospective observational study included patients with mRCC diagnosed between October 2014 and February 2024 who received IOIO or IOTKI treatment at Kobe University Hospital and 5 affiliated hospitals. Clinical and imaging data were collected, and target lesions were measured according to RECIST v.1.1 criteria. Time-dependent ROC curve analysis was performed to evaluate the prognostic value of tumor size, nephrectomy status, and IMDC risk criteria for progression-free survival (PFS) and overall survival (OS). ResultsThe study included 180 mRCC patients, consisting of 99 receiving IOIO therapy and 81 receiving IOTKI therapy. Time-dependent AUC analysis showed that tumor size had a higher predictive ability for PFS and OS in the IOIO group than the IOTKI group. In multivariate analysis, tumor size was a significant independent prognostic factor for PFS (HR: 1.010, 95% CI: 1.004–1.016, P < 0.001) in the IOIO group. Moreover, the AUC for tumor size was consistently superior in predicting outcomes compared to nephrectomy status and IMDC risk classification in the IOIO group. Kaplan-Meier curves indicated that tumor size effectively stratified PFS in both nephrectomized and non-nephrectomized cases. ConclusionTumor size significantly impacts the prognosis of mRCC patients treated with IOIO therapy, demonstrating greater predictive ability than nephrectomy status and IMDC risk classification. These findings suggest that tumor volume should be considered a critical factor in treatment decision-making for renal cancer, particularly in patients undergoing IOIO therapy.

Norwegian and Swedish value sets for the EORTC QLU-C10D utility instrument.

This study aimed to develop utility weights for the European Organization for Research and Treatment of Cancer (EORTC) QLU-C10D, a cancer-specific utility instrument, tailored to the Norwegian and Swedish populations. The utility weights are intended for use in the specific welfare contexts of Norway and Sweden to support more precise healthcare decision-making in cancer treatment and care. This cross-sectional study included 1019 Norwegian and 1048 Swedish participants representative in age and gender of the two general populations. Participants completed a discrete choice experiment involving 960 choice sets, each consisting of two EORTC QLU-C10D health states described by the instrument's domains and the duration of each state. Utility weights were calculated using generalized estimation equation models, and non-monotonic levels were merged to ensure consistent valuation. In the Norwegian participants, the largest utility decrements were seen for the domain of physical functioning (decrement of -0.263 for highest level "very much"), followed by pain (decrement -0.205 for level "very much") and role functioning (-0.139). Among the cancer-specific domains, nausea had the largest utility decrement (-0.124). In the Swedish participants, the largest utility decrements were also observed for physical functioning (-0.207 for the response "very much"), followed by pain (-0.139), role functioning (-0.133), and nausea (-0.119). Emotional functioning also exhibited a sizable utility decrement (-0.115). This study provides the first set of utility weights for the EORTC QLU-C10D specific to Norway and Sweden, reflecting the unique health preferences of these populations. The generated utility decrements can inform cost-utility analyses and optimize resource allocation in cancer care within the Norwegian and Swedish healthcare systems.

Predicting survival benefits of immune checkpoint inhibitor therapy in lung cancer patients: a machine learning approach using real-world data.

Due to the heterogeneity in the effectiveness of immunotherapy for lung cancer, identifying predictors is crucial. This study aimed to develop a machine learning model to identify predictors of overall survival in lung cancer patients treated with immune checkpoint inhibitors (ICIs). A retrospective analysis was performed on data from 1314 lung cancer patients at the Chongqing University Cancer Hospital from September 2018 to September 2022. We used the random survival forest (RSF) model to identify survival-influencing factors, using backward elimination for variable selection. A Cox proportional hazards (CPH) model was constructed using the most significant predictors. We assessed model performance and generalizability using time-dependent receiver operating characteristics (ROC) and predictive error curves. The RSF model demonstrated better predictive accuracy than the CPH (IBS 0.17 vs. 0.17; C-index 0.91 vs. 0.68), with better discrimination and prediction performance. The influential variables identified included D-dimer, Karnofsky performance status, albumin, surgery, TNM stage, platelet count, and age. The RSF model, which incorporated these variables, achieved area under the curve (AUC) scores of 0.95, 0.94, and 0.98 for 1-, 3-, and 5-year survival predictions, respectively, in the training set. The validation set showed AUCs of 0.94, 0.90, and 0.95, respectively, exceeding the performance of the CPH model. The study successfully developed a machine learning model that accurately predicted the survival benefits of ICI therapy in lung cancer patients, supporting clinical decision-making in lung cancer treatment.

Bridging Innovation and Education: IJMS Volume 12 Issue 3 and the 2024 World Conference on Medical Student Research (WCMSR)

The International Journal of Medical Students (IJMS) is proud to present Volume 12, Issue 3, highlighting innovative research across various medical disciplines. This issue underscores the journal's commitment to fostering medical education and advancing healthcare. Featured articles explore critical areas, including narrative medicine's role in reducing burnout, stress management among medical students, and the long-term impacts of the COVID-19 pandemic on cancer referrals and treatment decisions. Public health concerns such as antimicrobial resistance and access to syphilis screening in vulnerable populations are also examined. Additionally, this edition includes insightful reviews on pediatric allergy consultations and the utility of ultrasound in anatomical dissection courses. Case reports on rare medical conditions, reflections on global surgical electives, and personal experiences in overcoming research challenges further enrich the content. To complement this issue, the IJMS announces the 2024 World Conference on Medical Student Research (WCMSR), taking place on October 26, 2024, with participants from over 40 countries. This event promises to be a valuable platform for showcasing the best global research, fostering discussion, and inspiring the next generation of medical professionals.

The Encounter of Two Worlds: Divided Narratives of Decision-Making on Cancer Treatment Between Physicians and Patients.

Divided narratives pose long-standing difficulties in physician and patient communication. In decision-making on cancer treatment, divided narratives between physicians and patients hinder mutual understanding and agreement over the illness and its treatment. For effective decision-making on treatments, it is necessary to investigate the similarities and differences in these divided narratives. This study adopted a qualitative research design of narrative inquiry to examine the data, which included interviews with 32 cancer patients and 16 paired physicians in two hospitals in China. Data analysis was conducted using grounded theory to generate findings. Both physicians and patients were concerned about goals and obstacles to their decision-making on cancer treatment. Four common aspects of goal setting were identified from the divided narratives: decision pools, treatment goals, identity practice and preferred identity. Four common obstacles were identified: pains and trust, communication gap, financial issues and complex family. However, the meanings attached to these eight aspects differed between physicians and patients. Cancer treatment decision-making is an encounter of the scientific world and lifeworld. A divided narrative approach can identify the similarities and differences in the decision-making on cancer treatment between physicians and patients. Physicians generally adopt a rational decision-making approach, whereas patients generally adopt a relational decision-making approach. Despite the common concerns in their goals and obstacles, physicians and patients differed in their contextualized interpretations, which demonstrates the physicians' and the patients' pursuit of preferred identities in decision-making. The results of this study provide a new perspective to treatment decision-making, emphasizing the importance of narrative integration in reaching mutual agreement. The findings were shared with 15 cancer patients and caregivers for feedback and advice in June 2024. This study was also presented at the international conferences of COMET (International and Interdisciplinary Conference on Communication, Medicine, and Ethics) and ICCH (International Conference on Communication in Healthcare) 2023 for continuous feedback and comments.

Barriers and facilitators of healthcare professionals in integrating shared decision-making in pancreatic cancer treatment: A network approach.

Shared decision-making (SDM) is crucial in pancreatic cancer treatment due to its choice-sensitive nature and limited prognosis. Treatment of pancreatic cancer is organized in a network approach. Several obstacles exist on different levels-patient, healthcare professional, organizational, societal-that impede integration of SDM. This study aims to identify barriers and facilitators to SDM implementation within a comprehensive cancer network. A qualitative research design was applied, involving interviews and focus groups on barriers and facilitators with healthcare professionals involved in the implementation of SDM. In one comprehensive cancer network in the Netherlands, including seven hospitals, a project was initiated with the goal of empowering patients and healthcare professionals in SDM throughout primary, secondary and tertiary healthcare settings. A total of 17 participants were assessed. Directed qualitative content analysis was performed by two researchers. Main findings revealed barriers such as time constraints, lack of priority of physicians, little involvement of general practitioners, and insufficient social context of patients in referrals, alongside facilitators including learning communities with practical SDM examples, metro mapping, involvement of case manager in implementation and patient empowerment strategies. Addressing cultural, systemic barriers and developing innovative strategies are of importance to enhance SDM in pancreatic cancer treatment in a network approach. This study provides understanding of SDM implementation in complex healthcare settings and offers valuable guidance for future interventions seeking to improve decision-making processes in pancreatic cancer treatment and beyond.